• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.79-85
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• 2007

In this paper, a real-coded genetic algorithm is proposed for identification of time delay systems from step responses. FOPDT(First-Order Plus Dead-Time) and SOPDT(Second-Order Plus Dead-Time) systems, which are the most useful processes in this field, but are difficult for system identification because of a long dead-time problem and a model mismatch problem. Genetic algorithms have been successfully applied to a variety of complex optimization problems where other techniques have often failed. Thus, the modified crossover operator of a real-code genetic algorithm is proposed to effectively search the system parameters. The proposed method, using a real-coding genetic algorithm, shows better performance characteristics when compared to the usual area-based identification method and the directed identification method that uses step responses.

• Nuclear Engineering and Technology
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• v.36 no.1
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• pp.12-23
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• 2004

In the present study a new measurement technique has been developed, which uses an ultrasonic transmission signal in order to identify the vertical two phase flow pattern. The ultrasonic measurement system developed in the present study not only provides the information required for the identification of vertical two phase flow patterns but also makes real time identification possible. Various vertical two phase flow patterns such as bubbly, slug, churn, annular flow etc. have been accurately identified with the present ultrasonic measurement system under atmospheric condition. In addition, the present test apparatus can practically simulate the ultrasonic propagation characteristics under high temperature and high pressure systems. Therefore, it is expected that the present ultrasonic flow pattern identification technique could be applicable to the vertical two phase flow systems under high temperature and high pressure conditions.

• Structural Engineering and Mechanics
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• v.56 no.2
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• pp.293-315
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• 2015

While most of researches on system identification of building structures are aimed at finding modal parameters first and identifying the corresponding physical parameters by using the transformation in terms of transfer functions and cross spectra, etc., direct physical parameter system identification methods have been proposed recently. Due to the problem of signal/noise (SN) ratios, the previous methods are restricted mostly to earthquake records or forced vibration data. In this paper, a theoretical investigation is performed on the influence of wind disturbances on stiffness identification of building structures using micro-tremor at limited floors. It is concluded that the influence of wind disturbances on stiffness identification of building structures using micro-tremor at limited floors is restricted in case of using time-series data for low-rise buildings and does not cause serious problems.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.10
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• pp.918-922
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• 2007

Most of the study on system identification has been carried out using input/output relation in physical domain. However identification concept of stochastic system has not been reported up to now. Interest is focused to identify an unknown dynamic system under random external disturbances which is not possible to measure. A concept to identify the system parameters in stochastic domain is proposed and implemented in terms of simulation. Attempt has been made to identify the system parameters in inverse manner in stochastic domain based on system output only. Simulation is conducted to reveal quite noticeable performance of the proposed concept.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.289-300
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• 2003

In this paper, we introduce an identification method in Fuzzy Relation-based Fuzzy Neural Networks (FRFNN) through a hybrid identification algorithm. The proposed FRFNN modeling implement system structure and parameter identification in the efficient form of "If...., then... " statements, and exploit the theory of system optimization and fuzzy rules. The FRFNN modeling and identification environment realizes parameter identification through a synergistic usage of genetic optimization and complex search method. The hybrid identification algorithm is carried out by combining both genetic optimization and the improved complex method in order to guarantee both global optimization and local convergence. An aggregate objective function with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. The proposed model is experimented with using two nonlinear data. The obtained experimental results reveal that the proposed networks exhibit high accuracy and generalization capabilities in comparison to other models.er models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1310-1318
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• 2012

This study presents a new system that can make up for the decrease in identification rate due to the environmental or physical factors of a fingerprint identification system, which is most actively used among existing biometric systems. Considered most usable in life among many types of biometrics due to the convenience of use, smaller initial investment, and the smaller size, fingerprint identification is widely used in diverse applications. However, a fingerprint identification system has several defects, including environmental factors, physical skin damage, and variable sensor contact, lowering the identification rate in comparison to other biometric systems. On the basis of the existing fingerprint system, this study presents a more efficient system that can improve the identification rate by getting additional biometric information and three-dimensional finger type information through the use of simple and inexpensive additional equipment, which can be used in diverse areas.

• The Journal of Society for e-Business Studies
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• v.19 no.4
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• pp.151-168
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• 2014

UCI (Universal Content Identifier) as a national digital content identification system for identifying digital content and ICN (Integrated Copyright Number) for copyright management are operated by Korea copyright commission. However, They are not interoperable with international standard identification system such as ISRC (International Standard Recording Code), ISWC (International Standard Works Code), and ISAN (International Standard Audiovisual Number) due to lack of linkage system. Hence, extra works with international standard identification system are needed for international market entry and integrated management of statistical information including content settlement in international market is impossible. In this paper, the domestic/international identification systems are surveyed, and by metadata analysis of identification systems, linkage method with UCI/ICN is proposed. And also by developing the prototype model, research direction to UCI/ICN with international standard identification system is proposed.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1046-1050
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• 2003

In general, conventional user identification systems require users to carry a TAG or badge or to remember ID and password. Though biometric identification systems may relieve these problems, they are susceptible to environmental noise to some degree. We propose a natural user identification system, ubiFloor, exploiting user's walking pattern to identify the user. The system identifies a user, while tracking the user's location, with a set of simple ON/OFF switch sensors or equipments. Experimental results show that the proposed system can recognize the registered users at the rate of 92%. Future improvement in recognition rate may be achieved by combining other sensors such as camera, microphone, etc.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1165-1168
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• 1996

The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Network(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Radial Basis Function Networks(RBFN). The learning with RBFN is fast and precise. This paper discusses RBFN as identification procedure is based on a nonlinear dynamical systems. and A design method of model follow control system based on RBFN controller is developed. As a result of applying this method to inverted pendulum, the simulation has shown that RBFN can be used as identification and control of nonlinear dynamical systems effectively.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.290-292
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• 1993

This paper investigates the identification of discrete time nonlinear system using neural networks with two hidden layers. A New learning method of both NNI and NNC is proposed. For control of the dynamical system we use two neural networks, one for identification and the other for control, and proposed NN control system is based on a framework of MRC. We define a closed loop error. In the proposed learning method, the identification error and the closed loop error are utilized to train the NNI, whareas the control error and the closed loop error are used to train the NNC, The simulation results show that the identification and control schemes suggested are practically feasible and effective.